Based on the frequency spectrum of the nonlinear device output, the even-order distortion signals are found far from the fundamental signal so that they may be easily filtered out. On the other hand, the odd-order distortions are located very close to the fundamental so that it may be extremely hard to filter them out. Predistortion (PD) linearization has been proven to be an effective technique for reducing intermodulation distortion in nonlinear devices, such as power amplifiers, mixers, frequency multipliers, optical transmitters, and the like. Predistortion simply involves the creation of a distortion characteristic that is precisely complementary to the distortion characteristic of the nonlinear device, and cascading the two to ensure that the resulting system has little or no input-output distortion.
FIGS. 1A and 1B show two conventional approaches for the predistortion linearization of a PA that is a representative nonlinear device in a radio signal transmitter. In particular, FIG. 1A illustrates forward reference predistortion while FIG. 1B illustrates recursive reference predistortion.
The forward reference predistortion approach shown in FIG. 1A generally extracts the PA nonlinear characteristics by comparison of the input x(t) and the output y(t), deriving the pre-inverse function F{•} using time-consuming iterative methods to minimize the error. It is done by a digital signal processing (DSP) operating in conjunction with a look-up table. A complication of the predistortion approach of FIG. 1A is due to the phenomenon of memory effects in a PA. Memory effects are known as a serious impediment to predistortion linearization. Memory effects cause a hysteresis in the nonlinear transfer characteristics of a nonlinear device in response to past inputs. While deterministic, the net effect on the predistortion system is to create an apparent uncertainty in its response, thereby introducing some error in the model used to predistort the nonlinearity.
On the other hand, the recursive reference predistortion approach shown in FIG. 1B derives the nonlinearity by using z(t) as the reference for comparison, instead of x(t). Thus, the optimum predistortion function F{•} is given by the reciprocal of the complex gain function G{•}. The implementation of the reciprocal gain function is straightforward and can be done in analog domain so that memory effects are inherently compensated for in real time.
The two predistortion approaches of FIGS. 1A and 1B are based on the cascade predistortion on the same signal path, as shown in FIG. 2. In this case, it is intrinsically not easy to avoid cross disturbance between orthogonal signal predistorters (OPDs) since predistortion for each orthogonal signal is performed in a composite form, and there are difficulties to distinguish pure orthogonal signals and deal with predistortion on the same path.
Accordingly, there is a need in the industry for deterministic predistortion linearization that avoids cross-disturbance issues associated with conventional predistortion.